1 Data preparation

1.1 Outline

  • Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded

  • Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.

1.2 Load packages


library(reportfactory)
library(here)
library(rio) 
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)

1.3 Load scripts

These scripts will load:

  • all scripts stored as .R files inside /scripts/
  • all scripts stored as .R files inside /src/

These scripts also contain routines to access the latest clean encrypted data (see next section).


reportfactory::rfh_load_scripts()

1.4 Load clean data

We import the latest NHS pathways data:


x <- import_pathways() %>%
  as_tibble()
x
## # A tibble: 121,462 x 9
##    site_type date       sex    age   ccg_code ccg_name count postcode nhs_region
##    <chr>     <date>     <chr>  <chr> <chr>    <chr>    <int> <chr>    <chr>     
##  1 111       2020-03-18 female 0-18  e380000… nhs_bar…    35 rm13ae   london    
##  2 111       2020-03-18 female 0-18  e380000… nhs_bed…    27 mk454hr  east_of_e…
##  3 111       2020-03-18 female 0-18  e380000… nhs_bla…     9 bb12fd   north_west
##  4 111       2020-03-18 female 0-18  e380000… nhs_bro…    11 br33ql   london    
##  5 111       2020-03-18 female 0-18  e380000… nhs_can…     9 ws111jp  midlands  
##  6 111       2020-03-18 female 0-18  e380000… nhs_cit…    12 n15lz    london    
##  7 111       2020-03-18 female 0-18  e380000… nhs_enf…     7 en40dy   london    
##  8 111       2020-03-18 female 0-18  e380000… nhs_ham…     6 dl62uu   north_eas…
##  9 111       2020-03-18 female 0-18  e380000… nhs_har…    24 ts232la  north_eas…
## 10 111       2020-03-18 female 0-18  e380000… nhs_kin…     6 kt11eu   london    
## # … with 121,452 more rows

We also import demographics data for NHS regions in England, used later in our analysis:


path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
##                  nhs_region variable      value
## 1                North West     0-18 0.22538599
## 2  North East and Yorkshire     0-18 0.21876449
## 3                  Midlands     0-18 0.22564656
## 4           East of England     0-18 0.22810783
## 5                    London     0-18 0.23764782
## 6                South East     0-18 0.22458811
## 7                South West     0-18 0.20799797
## 8                North West    19-69 0.64274078
## 9  North East and Yorkshire    19-69 0.64437753
## 10                 Midlands    19-69 0.63876675
## 11          East of England    19-69 0.63034229
## 12                   London    19-69 0.67820084
## 13               South East    19-69 0.63267336
## 14               South West    19-69 0.63176131
## 15               North West   70-120 0.13187323
## 16 North East and Yorkshire   70-120 0.13685797
## 17                 Midlands   70-120 0.13558669
## 18          East of England   70-120 0.14154988
## 19                   London   70-120 0.08415135
## 20               South East   70-120 0.14273853
## 21               South West   70-120 0.16024072

Finally, we import publically available deaths per NHS region:


dth <- import_deaths() %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

#truncation to account for reporting delay
delay_max <- 21

dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
##     date_report               nhs_region deaths
## 1    2020-03-01          East of England      0
## 2    2020-03-02          East of England      1
## 3    2020-03-03          East of England      0
## 4    2020-03-04          East of England      0
## 5    2020-03-05          East of England      0
## 6    2020-03-06          East of England      1
## 7    2020-03-07          East of England      0
## 8    2020-03-08          East of England      0
## 9    2020-03-09          East of England      1
## 10   2020-03-10          East of England      0
## 11   2020-03-11          East of England      0
## 12   2020-03-12          East of England      0
## 13   2020-03-13          East of England      1
## 14   2020-03-14          East of England      2
## 15   2020-03-15          East of England      2
## 16   2020-03-16          East of England      1
## 17   2020-03-17          East of England      1
## 18   2020-03-18          East of England      5
## 19   2020-03-19          East of England      4
## 20   2020-03-20          East of England      2
## 21   2020-03-21          East of England     11
## 22   2020-03-22          East of England     11
## 23   2020-03-23          East of England     11
## 24   2020-03-24          East of England     19
## 25   2020-03-25          East of England     26
## 26   2020-03-26          East of England     36
## 27   2020-03-27          East of England     38
## 28   2020-03-28          East of England     28
## 29   2020-03-29          East of England     42
## 30   2020-03-30          East of England     45
## 31   2020-03-31          East of England     70
## 32   2020-04-01          East of England     61
## 33   2020-04-02          East of England     64
## 34   2020-04-03          East of England     80
## 35   2020-04-04          East of England     71
## 36   2020-04-05          East of England     76
## 37   2020-04-06          East of England     71
## 38   2020-04-07          East of England     92
## 39   2020-04-08          East of England    111
## 40   2020-04-09          East of England     87
## 41   2020-04-10          East of England     74
## 42   2020-04-11          East of England     91
## 43   2020-04-12          East of England    101
## 44   2020-04-13          East of England     77
## 45   2020-04-14          East of England     61
## 46   2020-04-15          East of England     82
## 47   2020-04-16          East of England     74
## 48   2020-04-17          East of England     86
## 49   2020-04-18          East of England     63
## 50   2020-04-19          East of England     66
## 51   2020-04-20          East of England     66
## 52   2020-04-21          East of England     74
## 53   2020-04-22          East of England     66
## 54   2020-04-23          East of England     49
## 55   2020-04-24          East of England     64
## 56   2020-04-25          East of England     54
## 57   2020-04-26          East of England     48
## 58   2020-04-27          East of England     46
## 59   2020-04-28          East of England     58
## 60   2020-04-29          East of England     32
## 61   2020-04-30          East of England     43
## 62   2020-05-01          East of England     49
## 63   2020-05-02          East of England     29
## 64   2020-05-03          East of England     41
## 65   2020-05-04          East of England     19
## 66   2020-05-05          East of England     35
## 67   2020-05-06          East of England     28
## 68   2020-05-07          East of England     33
## 69   2020-05-08          East of England     30
## 70   2020-05-09          East of England     26
## 71   2020-05-10          East of England     21
## 72   2020-05-11          East of England     18
## 73   2020-05-12          East of England     21
## 74   2020-05-13          East of England     27
## 75   2020-05-14          East of England     25
## 76   2020-05-15          East of England     18
## 77   2020-05-16          East of England     25
## 78   2020-05-17          East of England     15
## 79   2020-05-18          East of England     16
## 80   2020-05-19          East of England     14
## 81   2020-05-20          East of England     21
## 82   2020-05-21          East of England     18
## 83   2020-05-22          East of England      8
## 84   2020-05-23          East of England      5
## 85   2020-05-24          East of England      4
## 86   2020-03-01                   London      0
## 87   2020-03-02                   London      0
## 88   2020-03-03                   London      0
## 89   2020-03-04                   London      0
## 90   2020-03-05                   London      0
## 91   2020-03-06                   London      1
## 92   2020-03-07                   London      1
## 93   2020-03-08                   London      0
## 94   2020-03-09                   London      1
## 95   2020-03-10                   London      0
## 96   2020-03-11                   London      7
## 97   2020-03-12                   London      6
## 98   2020-03-13                   London     10
## 99   2020-03-14                   London     14
## 100  2020-03-15                   London     10
## 101  2020-03-16                   London     17
## 102  2020-03-17                   London     25
## 103  2020-03-18                   London     31
## 104  2020-03-19                   London     25
## 105  2020-03-20                   London     45
## 106  2020-03-21                   London     49
## 107  2020-03-22                   London     54
## 108  2020-03-23                   London     63
## 109  2020-03-24                   London     86
## 110  2020-03-25                   London    112
## 111  2020-03-26                   London    130
## 112  2020-03-27                   London    129
## 113  2020-03-28                   London    122
## 114  2020-03-29                   London    147
## 115  2020-03-30                   London    148
## 116  2020-03-31                   London    180
## 117  2020-04-01                   London    201
## 118  2020-04-02                   London    189
## 119  2020-04-03                   London    196
## 120  2020-04-04                   London    229
## 121  2020-04-05                   London    194
## 122  2020-04-06                   London    198
## 123  2020-04-07                   London    219
## 124  2020-04-08                   London    236
## 125  2020-04-09                   London    202
## 126  2020-04-10                   London    168
## 127  2020-04-11                   London    175
## 128  2020-04-12                   London    156
## 129  2020-04-13                   London    165
## 130  2020-04-14                   London    142
## 131  2020-04-15                   London    142
## 132  2020-04-16                   London    138
## 133  2020-04-17                   London     99
## 134  2020-04-18                   London    101
## 135  2020-04-19                   London    102
## 136  2020-04-20                   London     94
## 137  2020-04-21                   London     93
## 138  2020-04-22                   London    108
## 139  2020-04-23                   London     77
## 140  2020-04-24                   London     71
## 141  2020-04-25                   London     57
## 142  2020-04-26                   London     53
## 143  2020-04-27                   London     51
## 144  2020-04-28                   London     43
## 145  2020-04-29                   London     43
## 146  2020-04-30                   London     39
## 147  2020-05-01                   London     41
## 148  2020-05-02                   London     40
## 149  2020-05-03                   London     35
## 150  2020-05-04                   London     29
## 151  2020-05-05                   London     25
## 152  2020-05-06                   London     34
## 153  2020-05-07                   London     35
## 154  2020-05-08                   London     29
## 155  2020-05-09                   London     22
## 156  2020-05-10                   London     25
## 157  2020-05-11                   London     16
## 158  2020-05-12                   London     16
## 159  2020-05-13                   London     16
## 160  2020-05-14                   London     20
## 161  2020-05-15                   London     17
## 162  2020-05-16                   London     13
## 163  2020-05-17                   London     15
## 164  2020-05-18                   London      9
## 165  2020-05-19                   London     12
## 166  2020-05-20                   London     18
## 167  2020-05-21                   London     11
## 168  2020-05-22                   London      5
## 169  2020-05-23                   London      4
## 170  2020-05-24                   London      1
## 171  2020-03-01                 Midlands      0
## 172  2020-03-02                 Midlands      0
## 173  2020-03-03                 Midlands      1
## 174  2020-03-04                 Midlands      0
## 175  2020-03-05                 Midlands      0
## 176  2020-03-06                 Midlands      0
## 177  2020-03-07                 Midlands      0
## 178  2020-03-08                 Midlands      3
## 179  2020-03-09                 Midlands      1
## 180  2020-03-10                 Midlands      0
## 181  2020-03-11                 Midlands      2
## 182  2020-03-12                 Midlands      6
## 183  2020-03-13                 Midlands      5
## 184  2020-03-14                 Midlands      4
## 185  2020-03-15                 Midlands      5
## 186  2020-03-16                 Midlands     11
## 187  2020-03-17                 Midlands      8
## 188  2020-03-18                 Midlands     13
## 189  2020-03-19                 Midlands      8
## 190  2020-03-20                 Midlands     28
## 191  2020-03-21                 Midlands     13
## 192  2020-03-22                 Midlands     31
## 193  2020-03-23                 Midlands     33
## 194  2020-03-24                 Midlands     41
## 195  2020-03-25                 Midlands     48
## 196  2020-03-26                 Midlands     64
## 197  2020-03-27                 Midlands     72
## 198  2020-03-28                 Midlands     89
## 199  2020-03-29                 Midlands     92
## 200  2020-03-30                 Midlands     90
## 201  2020-03-31                 Midlands    123
## 202  2020-04-01                 Midlands    140
## 203  2020-04-02                 Midlands    142
## 204  2020-04-03                 Midlands    124
## 205  2020-04-04                 Midlands    150
## 206  2020-04-05                 Midlands    164
## 207  2020-04-06                 Midlands    140
## 208  2020-04-07                 Midlands    123
## 209  2020-04-08                 Midlands    185
## 210  2020-04-09                 Midlands    138
## 211  2020-04-10                 Midlands    127
## 212  2020-04-11                 Midlands    142
## 213  2020-04-12                 Midlands    138
## 214  2020-04-13                 Midlands    120
## 215  2020-04-14                 Midlands    116
## 216  2020-04-15                 Midlands    147
## 217  2020-04-16                 Midlands    101
## 218  2020-04-17                 Midlands    118
## 219  2020-04-18                 Midlands    115
## 220  2020-04-19                 Midlands     91
## 221  2020-04-20                 Midlands    107
## 222  2020-04-21                 Midlands     86
## 223  2020-04-22                 Midlands     77
## 224  2020-04-23                 Midlands    102
## 225  2020-04-24                 Midlands     77
## 226  2020-04-25                 Midlands     72
## 227  2020-04-26                 Midlands     81
## 228  2020-04-27                 Midlands     74
## 229  2020-04-28                 Midlands     68
## 230  2020-04-29                 Midlands     53
## 231  2020-04-30                 Midlands     53
## 232  2020-05-01                 Midlands     64
## 233  2020-05-02                 Midlands     51
## 234  2020-05-03                 Midlands     50
## 235  2020-05-04                 Midlands     60
## 236  2020-05-05                 Midlands     58
## 237  2020-05-06                 Midlands     56
## 238  2020-05-07                 Midlands     48
## 239  2020-05-08                 Midlands     34
## 240  2020-05-09                 Midlands     37
## 241  2020-05-10                 Midlands     41
## 242  2020-05-11                 Midlands     32
## 243  2020-05-12                 Midlands     45
## 244  2020-05-13                 Midlands     38
## 245  2020-05-14                 Midlands     32
## 246  2020-05-15                 Midlands     38
## 247  2020-05-16                 Midlands     34
## 248  2020-05-17                 Midlands     30
## 249  2020-05-18                 Midlands     33
## 250  2020-05-19                 Midlands     31
## 251  2020-05-20                 Midlands     33
## 252  2020-05-21                 Midlands     27
## 253  2020-05-22                 Midlands     16
## 254  2020-05-23                 Midlands     14
## 255  2020-05-24                 Midlands      3
## 256  2020-03-01 North East and Yorkshire      0
## 257  2020-03-02 North East and Yorkshire      0
## 258  2020-03-03 North East and Yorkshire      0
## 259  2020-03-04 North East and Yorkshire      0
## 260  2020-03-05 North East and Yorkshire      0
## 261  2020-03-06 North East and Yorkshire      0
## 262  2020-03-07 North East and Yorkshire      0
## 263  2020-03-08 North East and Yorkshire      0
## 264  2020-03-09 North East and Yorkshire      0
## 265  2020-03-10 North East and Yorkshire      0
## 266  2020-03-11 North East and Yorkshire      0
## 267  2020-03-12 North East and Yorkshire      0
## 268  2020-03-13 North East and Yorkshire      0
## 269  2020-03-14 North East and Yorkshire      0
## 270  2020-03-15 North East and Yorkshire      2
## 271  2020-03-16 North East and Yorkshire      3
## 272  2020-03-17 North East and Yorkshire      1
## 273  2020-03-18 North East and Yorkshire      2
## 274  2020-03-19 North East and Yorkshire      6
## 275  2020-03-20 North East and Yorkshire      5
## 276  2020-03-21 North East and Yorkshire      6
## 277  2020-03-22 North East and Yorkshire      7
## 278  2020-03-23 North East and Yorkshire      9
## 279  2020-03-24 North East and Yorkshire      7
## 280  2020-03-25 North East and Yorkshire     18
## 281  2020-03-26 North East and Yorkshire     21
## 282  2020-03-27 North East and Yorkshire     28
## 283  2020-03-28 North East and Yorkshire     35
## 284  2020-03-29 North East and Yorkshire     38
## 285  2020-03-30 North East and Yorkshire     64
## 286  2020-03-31 North East and Yorkshire     60
## 287  2020-04-01 North East and Yorkshire     67
## 288  2020-04-02 North East and Yorkshire     74
## 289  2020-04-03 North East and Yorkshire     99
## 290  2020-04-04 North East and Yorkshire    104
## 291  2020-04-05 North East and Yorkshire     92
## 292  2020-04-06 North East and Yorkshire     95
## 293  2020-04-07 North East and Yorkshire    102
## 294  2020-04-08 North East and Yorkshire    107
## 295  2020-04-09 North East and Yorkshire    111
## 296  2020-04-10 North East and Yorkshire    117
## 297  2020-04-11 North East and Yorkshire     98
## 298  2020-04-12 North East and Yorkshire     84
## 299  2020-04-13 North East and Yorkshire     94
## 300  2020-04-14 North East and Yorkshire    107
## 301  2020-04-15 North East and Yorkshire     95
## 302  2020-04-16 North East and Yorkshire    103
## 303  2020-04-17 North East and Yorkshire     86
## 304  2020-04-18 North East and Yorkshire     95
## 305  2020-04-19 North East and Yorkshire     87
## 306  2020-04-20 North East and Yorkshire    100
## 307  2020-04-21 North East and Yorkshire     76
## 308  2020-04-22 North East and Yorkshire     83
## 309  2020-04-23 North East and Yorkshire     62
## 310  2020-04-24 North East and Yorkshire     72
## 311  2020-04-25 North East and Yorkshire     68
## 312  2020-04-26 North East and Yorkshire     63
## 313  2020-04-27 North East and Yorkshire     65
## 314  2020-04-28 North East and Yorkshire     57
## 315  2020-04-29 North East and Yorkshire     69
## 316  2020-04-30 North East and Yorkshire     56
## 317  2020-05-01 North East and Yorkshire     64
## 318  2020-05-02 North East and Yorkshire     48
## 319  2020-05-03 North East and Yorkshire     39
## 320  2020-05-04 North East and Yorkshire     48
## 321  2020-05-05 North East and Yorkshire     40
## 322  2020-05-06 North East and Yorkshire     50
## 323  2020-05-07 North East and Yorkshire     41
## 324  2020-05-08 North East and Yorkshire     38
## 325  2020-05-09 North East and Yorkshire     43
## 326  2020-05-10 North East and Yorkshire     39
## 327  2020-05-11 North East and Yorkshire     28
## 328  2020-05-12 North East and Yorkshire     25
## 329  2020-05-13 North East and Yorkshire     27
## 330  2020-05-14 North East and Yorkshire     28
## 331  2020-05-15 North East and Yorkshire     30
## 332  2020-05-16 North East and Yorkshire     35
## 333  2020-05-17 North East and Yorkshire     26
## 334  2020-05-18 North East and Yorkshire     26
## 335  2020-05-19 North East and Yorkshire     27
## 336  2020-05-20 North East and Yorkshire     20
## 337  2020-05-21 North East and Yorkshire     29
## 338  2020-05-22 North East and Yorkshire     19
## 339  2020-05-23 North East and Yorkshire     14
## 340  2020-05-24 North East and Yorkshire      7
## 341  2020-03-01               North West      0
## 342  2020-03-02               North West      0
## 343  2020-03-03               North West      0
## 344  2020-03-04               North West      0
## 345  2020-03-05               North West      1
## 346  2020-03-06               North West      0
## 347  2020-03-07               North West      0
## 348  2020-03-08               North West      1
## 349  2020-03-09               North West      0
## 350  2020-03-10               North West      0
## 351  2020-03-11               North West      0
## 352  2020-03-12               North West      2
## 353  2020-03-13               North West      3
## 354  2020-03-14               North West      1
## 355  2020-03-15               North West      4
## 356  2020-03-16               North West      2
## 357  2020-03-17               North West      4
## 358  2020-03-18               North West      6
## 359  2020-03-19               North West      6
## 360  2020-03-20               North West     10
## 361  2020-03-21               North West     11
## 362  2020-03-22               North West     13
## 363  2020-03-23               North West     15
## 364  2020-03-24               North West     21
## 365  2020-03-25               North West     20
## 366  2020-03-26               North West     29
## 367  2020-03-27               North West     35
## 368  2020-03-28               North West     27
## 369  2020-03-29               North West     46
## 370  2020-03-30               North West     66
## 371  2020-03-31               North West     52
## 372  2020-04-01               North West     85
## 373  2020-04-02               North West     95
## 374  2020-04-03               North West     94
## 375  2020-04-04               North West     98
## 376  2020-04-05               North West    102
## 377  2020-04-06               North West    100
## 378  2020-04-07               North West    133
## 379  2020-04-08               North West    123
## 380  2020-04-09               North West    118
## 381  2020-04-10               North West    115
## 382  2020-04-11               North West    135
## 383  2020-04-12               North West    126
## 384  2020-04-13               North West    125
## 385  2020-04-14               North West    130
## 386  2020-04-15               North West    114
## 387  2020-04-16               North West    133
## 388  2020-04-17               North West     96
## 389  2020-04-18               North West    112
## 390  2020-04-19               North West     70
## 391  2020-04-20               North West     80
## 392  2020-04-21               North West     75
## 393  2020-04-22               North West     80
## 394  2020-04-23               North West     85
## 395  2020-04-24               North West     65
## 396  2020-04-25               North West     65
## 397  2020-04-26               North West     54
## 398  2020-04-27               North West     54
## 399  2020-04-28               North West     56
## 400  2020-04-29               North West     62
## 401  2020-04-30               North West     57
## 402  2020-05-01               North West     43
## 403  2020-05-02               North West     55
## 404  2020-05-03               North West     54
## 405  2020-05-04               North West     44
## 406  2020-05-05               North West     46
## 407  2020-05-06               North West     41
## 408  2020-05-07               North West     44
## 409  2020-05-08               North West     40
## 410  2020-05-09               North West     28
## 411  2020-05-10               North West     38
## 412  2020-05-11               North West     32
## 413  2020-05-12               North West     35
## 414  2020-05-13               North West     24
## 415  2020-05-14               North West     26
## 416  2020-05-15               North West     33
## 417  2020-05-16               North West     30
## 418  2020-05-17               North West     23
## 419  2020-05-18               North West     26
## 420  2020-05-19               North West     31
## 421  2020-05-20               North West     23
## 422  2020-05-21               North West     20
## 423  2020-05-22               North West     14
## 424  2020-05-23               North West     12
## 425  2020-05-24               North West      2
## 426  2020-03-01               South East      0
## 427  2020-03-02               South East      0
## 428  2020-03-03               South East      1
## 429  2020-03-04               South East      0
## 430  2020-03-05               South East      1
## 431  2020-03-06               South East      0
## 432  2020-03-07               South East      0
## 433  2020-03-08               South East      1
## 434  2020-03-09               South East      1
## 435  2020-03-10               South East      1
## 436  2020-03-11               South East      1
## 437  2020-03-12               South East      0
## 438  2020-03-13               South East      1
## 439  2020-03-14               South East      1
## 440  2020-03-15               South East      5
## 441  2020-03-16               South East      8
## 442  2020-03-17               South East      7
## 443  2020-03-18               South East     10
## 444  2020-03-19               South East      9
## 445  2020-03-20               South East     13
## 446  2020-03-21               South East      7
## 447  2020-03-22               South East     25
## 448  2020-03-23               South East     20
## 449  2020-03-24               South East     22
## 450  2020-03-25               South East     28
## 451  2020-03-26               South East     34
## 452  2020-03-27               South East     34
## 453  2020-03-28               South East     36
## 454  2020-03-29               South East     54
## 455  2020-03-30               South East     58
## 456  2020-03-31               South East     65
## 457  2020-04-01               South East     65
## 458  2020-04-02               South East     55
## 459  2020-04-03               South East     72
## 460  2020-04-04               South East     80
## 461  2020-04-05               South East     81
## 462  2020-04-06               South East     87
## 463  2020-04-07               South East     99
## 464  2020-04-08               South East     82
## 465  2020-04-09               South East    104
## 466  2020-04-10               South East     88
## 467  2020-04-11               South East     87
## 468  2020-04-12               South East     88
## 469  2020-04-13               South East     83
## 470  2020-04-14               South East     64
## 471  2020-04-15               South East     72
## 472  2020-04-16               South East     56
## 473  2020-04-17               South East     86
## 474  2020-04-18               South East     57
## 475  2020-04-19               South East     69
## 476  2020-04-20               South East     85
## 477  2020-04-21               South East     49
## 478  2020-04-22               South East     54
## 479  2020-04-23               South East     57
## 480  2020-04-24               South East     64
## 481  2020-04-25               South East     50
## 482  2020-04-26               South East     51
## 483  2020-04-27               South East     40
## 484  2020-04-28               South East     40
## 485  2020-04-29               South East     46
## 486  2020-04-30               South East     28
## 487  2020-05-01               South East     37
## 488  2020-05-02               South East     35
## 489  2020-05-03               South East     17
## 490  2020-05-04               South East     35
## 491  2020-05-05               South East     29
## 492  2020-05-06               South East     22
## 493  2020-05-07               South East     25
## 494  2020-05-08               South East     25
## 495  2020-05-09               South East     28
## 496  2020-05-10               South East     19
## 497  2020-05-11               South East     23
## 498  2020-05-12               South East     26
## 499  2020-05-13               South East     17
## 500  2020-05-14               South East     31
## 501  2020-05-15               South East     23
## 502  2020-05-16               South East     18
## 503  2020-05-17               South East     16
## 504  2020-05-18               South East     17
## 505  2020-05-19               South East     12
## 506  2020-05-20               South East     21
## 507  2020-05-21               South East     10
## 508  2020-05-22               South East      9
## 509  2020-05-23               South East      3
## 510  2020-05-24               South East      2
## 511  2020-03-01               South West      0
## 512  2020-03-02               South West      0
## 513  2020-03-03               South West      0
## 514  2020-03-04               South West      0
## 515  2020-03-05               South West      0
## 516  2020-03-06               South West      0
## 517  2020-03-07               South West      0
## 518  2020-03-08               South West      0
## 519  2020-03-09               South West      0
## 520  2020-03-10               South West      0
## 521  2020-03-11               South West      1
## 522  2020-03-12               South West      0
## 523  2020-03-13               South West      0
## 524  2020-03-14               South West      1
## 525  2020-03-15               South West      0
## 526  2020-03-16               South West      0
## 527  2020-03-17               South West      2
## 528  2020-03-18               South West      2
## 529  2020-03-19               South West      4
## 530  2020-03-20               South West      3
## 531  2020-03-21               South West      6
## 532  2020-03-22               South West      9
## 533  2020-03-23               South West      9
## 534  2020-03-24               South West      7
## 535  2020-03-25               South West      9
## 536  2020-03-26               South West     11
## 537  2020-03-27               South West     13
## 538  2020-03-28               South West     21
## 539  2020-03-29               South West     18
## 540  2020-03-30               South West     23
## 541  2020-03-31               South West     23
## 542  2020-04-01               South West     22
## 543  2020-04-02               South West     23
## 544  2020-04-03               South West     30
## 545  2020-04-04               South West     42
## 546  2020-04-05               South West     32
## 547  2020-04-06               South West     34
## 548  2020-04-07               South West     39
## 549  2020-04-08               South West     47
## 550  2020-04-09               South West     24
## 551  2020-04-10               South West     46
## 552  2020-04-11               South West     43
## 553  2020-04-12               South West     23
## 554  2020-04-13               South West     26
## 555  2020-04-14               South West     24
## 556  2020-04-15               South West     31
## 557  2020-04-16               South West     29
## 558  2020-04-17               South West     33
## 559  2020-04-18               South West     25
## 560  2020-04-19               South West     31
## 561  2020-04-20               South West     26
## 562  2020-04-21               South West     26
## 563  2020-04-22               South West     22
## 564  2020-04-23               South West     17
## 565  2020-04-24               South West     19
## 566  2020-04-25               South West     15
## 567  2020-04-26               South West     27
## 568  2020-04-27               South West     13
## 569  2020-04-28               South West     17
## 570  2020-04-29               South West     14
## 571  2020-04-30               South West     26
## 572  2020-05-01               South West      6
## 573  2020-05-02               South West      6
## 574  2020-05-03               South West     10
## 575  2020-05-04               South West     16
## 576  2020-05-05               South West     14
## 577  2020-05-06               South West     18
## 578  2020-05-07               South West     16
## 579  2020-05-08               South West      5
## 580  2020-05-09               South West     10
## 581  2020-05-10               South West      5
## 582  2020-05-11               South West      7
## 583  2020-05-12               South West      7
## 584  2020-05-13               South West      7
## 585  2020-05-14               South West      6
## 586  2020-05-15               South West      3
## 587  2020-05-16               South West      4
## 588  2020-05-17               South West      6
## 589  2020-05-18               South West      4
## 590  2020-05-19               South West      6
## 591  2020-05-20               South West      1
## 592  2020-05-21               South West      8
## 593  2020-05-22               South West      4
## 594  2020-05-23               South West      4
## 595  2020-05-24               South West      0

1.5 Completion date

We extract the completion date from the NHS Pathways file timestamp:


database_date <- attr(x, "timestamp")
database_date
## [1] "2020-05-25"

The completion date of the NHS Pathways data is Monday 25 May 2020.

1.6 Add variables

We add the following variable:

  • day: an integer representing the number of days from the earliest data reported, used for modelling purposes; the first day is 0

x <- x %>% 
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)),
         nhs_region = gsub(" Of ", " of ", nhs_region),
         nhs_region = gsub(" And ", " and ", nhs_region),
         day = as.integer(date - min(date, na.rm = TRUE)))

1.7 Auxiliary functions

These are functions which will be used further in the analyses.

Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:


## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here

Rsq <- function(x) {
  1 - (x$deviance / x$null.deviance)
}

Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:


## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals

get_r <- function(model) {
  ##  extract coefficients and conf int
  out <- data.frame(r = coef(model))  %>%
    rownames_to_column("var") %>% 
    cbind(confint(model)) %>%
    filter(!grepl("day_of_week", var)) %>% 
    filter(grepl("day", var)) %>%
    rename(lower_95 = "2.5 %",
           upper_95 = "97.5 %") %>%
    mutate(var = sub("day:", "", var))
  
  ## reconstruct values: intercept + region-coefficient
  for (i in 2:nrow(out)) {
    out[i, -1] <- out[1, -1] + out[i, -1]
  }
  
  ## find the name of the intercept, restore regions names
  out <- out %>%
    mutate(nhs_region = model$xlevels$nhs_region) %>%
    select(nhs_region, everything(), -var)
  
  ## find halving times
  halving <- log(0.5) / out[,-1] %>%
    rename(halving_t = r,
           halving_t_lower_95 = lower_95,
           halving_t_upper_95 = upper_95)
  
  ## set halving times with exclusion intervals to NA
  no_halving <- out$lower_95 < 0 & out$upper_95 > 0
  halving[no_halving, ] <- NA_real_
  
  ## return all data
  cbind(out, halving)
  
}

Functions used in the correlation analysis between NHS Pathways reports and deaths:

## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.

getcor <- function(x, ndx) {
  return(cor(x$deaths[ndx],
             x$note_lag[ndx],
             use = "complete.obs",
             method = "pearson"))
}

## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)

getboot <- function(x) {
  result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000), 
                           type = "bca")
  return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
                    r = result$t0,
                    r_low = result$bca[4],
                    r_hi = result$bca[5]))
}

Function to classify the day of the week into weekend, Monday, and the rest:


## Fn to add day of week
day_of_week <- function(df) {
  df %>% 
    dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>% 
    dplyr::mutate(day_of_week = dplyr::case_when(
      day_of_week %in% c("Sat", "Sun") ~ "weekend",
      day_of_week %in% c("Mon") ~ "monday",
      !(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
    ) %>% 
      factor(levels = c("rest_of_week", "monday", "weekend")))
}

Custom color palettes, color scales, and vectors of colors:


pal <- c("#006212",
         "#ae3cab",
         "#00db90",
         "#960c00",
         "#55aaff",
         "#ff7e78",
         "#00388d")

age.pal <- viridis::viridis(3,begin = 0.1, end = 0.7)

3 Comparison with deaths time series

3.1 Outline

We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.

Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.

3.2 Lagged correlation

We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.

First we join the NHS Pathways and death data, and aggregate over all England:

## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max

dth_trunc <- dth %>%
  rename(date = date_report) %>%
  filter(date <= trunc_date) 

## join with notification data
all_data <- x %>% 
  filter(!is.na(nhs_region)) %>%
  group_by(date, nhs_region) %>%
  summarise(count = sum(count, na.rm = T)) %>%
  ungroup %>%
  inner_join(dth_trunc,
             by = c("date","nhs_region"))

all_tot <- all_data %>%
  group_by(date) %>%
  summarise(count = sum(count, na.rm = TRUE),
            deaths = sum(deaths, na.rm = TRUE)) 

We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:


## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
  
  ## lag reports
  summary <- all_tot %>% 
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI
    getboot(.) %>%
    mutate(lag = i)

  lag_cor <- bind_rows(lag_cor, summary)
}

cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
  theme_bw() +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_point() +
  geom_line() +
  labs(x = "Lag between NHS pathways and death data (days)",
       y = "Pearson's correlation") +
  large_txt
cor_vs_lag


l_opt <- which.max(lag_cor$r)

This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.


all_tot <- all_tot %>%
  rename(date_death = date) %>%
  mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
         note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
         date_note = lag(date_death,16))

lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")

summary(lag_mod)
## 
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.8282  -1.9044  -0.2141   2.1424   6.3004  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.653e+00  5.543e-02   102.0  < 2e-16 ***
## note_lag    7.480e-06  5.267e-07    14.2 1.37e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 8.676007)
## 
##     Null deviance: 2016.70  on 30  degrees of freedom
## Residual deviance:  253.29  on 29  degrees of freedom
##   (16 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 4

exp(coefficients(lag_mod))
## (Intercept)    note_lag 
##  285.250262    1.000007
exp(confint(lag_mod))
##                  2.5 %     97.5 %
## (Intercept) 255.631813 317.682623
## note_lag      1.000006   1.000009

Rsq(lag_mod)
## [1] 0.8744048

mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])

all_tot_pred <- 
  all_tot %>%
  filter(!is.na(note_lag)) %>%
  mutate(pred = mod_fit$fit,
         pred.se = mod_fit$se.fit,
         low = exp(pred - 1.96*pred.se),
         hi = exp(pred + 1.96*pred.se))


glm_fit <- all_tot_pred %>% 
    filter(!is.na(note_lag)) %>%
  ggplot(aes(x = note_lag, y = deaths)) +
  geom_point() + 
  geom_line(aes(y = exp(pred))) + 
  geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
  theme_bw() +
  labs(y = "Daily number of\ndeaths reported",
       x = "Daily number of NHS Pathways reports") +
  large_txt

glm_fit

4 Supplementary figures

4.1 Serial interval distribution

This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.

SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale, w = 0.5)

SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
                                        meanlog = log(4.7),
                                        sdlog = log(2.9), w = 0.5)

SI_dist1 <- data.frame(x = SI_distribution$r(1e5)) 
SI_dist1 <- count(SI_dist1, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 30, 5)) +
    theme_bw()

SI_dist2 <- data.frame(x = SI_distribution2$r(1e5)) 
SI_dist2 <- count(SI_dist2, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
    theme_bw()


ggpubr::ggarrange(SI_dist1,
                  SI_dist2,
                  nrow = 1,
                  labels = "AUTO") 

4.2 Sensitivity analysis - 7 or 21 days moving window

We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.

First with the 7 days window:

## set moving time window (1/2/3 weeks)
w <- 7

# create empty df
r_all_sliding_7days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
plot_R <- r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_7days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_7days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_7 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

Then with the 21 days window:

## set moving time window (1/2/3 weeks)
w <- 21

# create empty df
r_all_sliding_21days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
# plot
plot_R <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_21days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_21days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_21 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

And we combine both outputs into a single plot:


ggpubr::ggarrange(r_R_7,
                  r_R_21,
                  nrow = 2,
                  labels = "AUTO",
                  common.legend = TRUE,
                  legend = "bottom") 

4.3 Correlation between NHS Pathways reports and deaths by NHS region


lag_cor_reg <- data.frame()

for (i in 0:30) {

  summary <-
    all_data %>%
    group_by(nhs_region) %>%
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI for each region
    group_modify(~getboot(.x)) %>%
    mutate(lag = i)
  
  lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}

cor_vs_lag_reg <- 
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
  geom_point() +
  geom_line() +
  facet_wrap(~nhs_region) +
  scale_color_manual(values = pal) +
  scale_fill_manual(values = pal, guide = F) +  
  theme_bw() +
  labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(override.aes = list(fill = NA)))

cor_vs_lag_reg

5 Export data

We save the tables created during our analysis:


if (!dir.exists("excel_tables")) {
  dir.create("excel_tables")
}


## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")

for (e in tables_to_export) {
  rio::export(get(e),
              file.path("excel_tables",
                        paste0(e, ".xlsx")))
}

## also export result from regression on lagged data 
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))

6 System information

6.1 Outline

The following information documents the system on which the document was compiled.

6.2 System

This provides information on the operating system.

Sys.info()
##                                                                                             sysname 
##                                                                                            "Darwin" 
##                                                                                             release 
##                                                                                            "19.4.0" 
##                                                                                             version 
## "Darwin Kernel Version 19.4.0: Wed Mar  4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64" 
##                                                                                            nodename 
##                                                                                    "Mac-1583.local" 
##                                                                                             machine 
##                                                                                            "x86_64" 
##                                                                                               login 
##                                                                                              "root" 
##                                                                                                user 
##                                                                                            "runner" 
##                                                                                      effective_user 
##                                                                                            "runner"

6.3 R environment

This provides information on the version of R used:

R.version
##                _                           
## platform       x86_64-apple-darwin15.6.0   
## arch           x86_64                      
## os             darwin15.6.0                
## system         x86_64, darwin15.6.0        
## status                                     
## major          3                           
## minor          6.3                         
## year           2020                        
## month          02                          
## day            29                          
## svn rev        77875                       
## language       R                           
## version.string R version 3.6.3 (2020-02-29)
## nickname       Holding the Windsock

6.4 R packages

This provides information on the packages used:

sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggnewscale_0.4.1     ggpubr_0.3.0         lubridate_1.7.8     
##  [4] chngpt_2020.5-21     cyphr_1.1.0          DT_0.13             
##  [7] kableExtra_1.1.0     janitor_2.0.1        remotes_2.1.1       
## [10] projections_0.4.1    earlyR_0.0.1         epitrix_0.2.2       
## [13] distcrete_1.0.3      incidence_1.7.1      rio_0.5.16          
## [16] reshape2_1.4.4       rvest_0.3.5          xml2_1.3.2          
## [19] linelist_0.0.40.9000 forcats_0.5.0        stringr_1.4.0       
## [22] dplyr_0.8.5          purrr_0.3.4          readr_1.3.1         
## [25] tidyr_1.1.0          tibble_3.0.1         ggplot2_3.3.0       
## [28] tidyverse_1.3.0      here_0.1             reportfactory_0.0.5 
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1  selectr_0.4-2     ggsignif_0.6.0    ellipsis_0.3.1   
##  [5] rprojroot_1.3-2   snakecase_0.11.0  fs_1.4.1          rstudioapi_0.11  
##  [9] farver_2.0.3      fansi_0.4.1       splines_3.6.3     knitr_1.28       
## [13] jsonlite_1.6.1    broom_0.5.6       dbplyr_1.4.3      compiler_3.6.3   
## [17] httr_1.4.1        backports_1.1.7   assertthat_0.2.1  Matrix_1.2-18    
## [21] cli_2.0.2         htmltools_0.4.0   prettyunits_1.1.1 tools_3.6.3      
## [25] gtable_0.3.0      glue_1.4.1        Rcpp_1.0.4.6      carData_3.0-4    
## [29] cellranger_1.1.0  vctrs_0.3.0       nlme_3.1-144      matchmaker_0.1.1 
## [33] crosstalk_1.1.0.1 xfun_0.14         ps_1.3.3          openxlsx_4.1.5   
## [37] lifecycle_0.2.0   rstatix_0.5.0     MASS_7.3-51.5     scales_1.1.1     
## [41] hms_0.5.3         sodium_1.1        yaml_2.2.1        curl_4.3         
## [45] gridExtra_2.3     stringi_1.4.6     kyotil_2019.11-22 boot_1.3-24      
## [49] pkgbuild_1.0.8    zip_2.0.4         rlang_0.4.6       pkgconfig_2.0.3  
## [53] evaluate_0.14     lattice_0.20-38   labeling_0.3      htmlwidgets_1.5.1
## [57] cowplot_1.0.0     processx_3.4.2    tidyselect_1.1.0  plyr_1.8.6       
## [61] magrittr_1.5      R6_2.4.1          generics_0.0.2    DBI_1.1.0        
## [65] pillar_1.4.4      haven_2.3.0       foreign_0.8-75    withr_2.2.0      
## [69] mgcv_1.8-31       survival_3.1-8    abind_1.4-5       modelr_0.1.8     
## [73] crayon_1.3.4      car_3.0-8         utf8_1.1.4        rmarkdown_2.1    
## [77] viridis_0.5.1     grid_3.6.3        readxl_1.3.1      data.table_1.12.8
## [81] callr_3.4.3       reprex_0.3.0      digest_0.6.25     webshot_0.5.2    
## [85] munsell_0.5.0     viridisLite_0.3.0